TRACTOR
Precision farming in protected environments with autonomous and collaborative rovers
Scheda del progetto
Particle srl
Micro aerial vehicles technology srl
Societa' Agricola San Pietro di Lingua Carlo e c.
TRACTOR aims to develop a digital technological solution capable of optimizing and improving current production and management systems in agricultural businesses. Specifically, TRACTOR plans to develop applications based on the use of an autonomous and collaborative rover, along with AI (artificial intelligence) solutions, to continuously monitor blueberry plants within protected environments, in order to support the decision-making processes of agricultural enterprises.
Contacts:
Valerio Verderosa
valerio.verderosa@we-plus.eu
The project aims to demonstrate the following use cases: estimating the number of fruits and monitoring the ripening stage; monitoring the plant vigor index; supporting operators during harvest. To enrich the collected data with positional information and to enable autonomous navigation of the rover within protected environments (e.g., greenhouses), it is necessary to integrate a precise localization system based on UWB technology, as GNSS signals are heavily disrupted by the metal structure.
The evolution of the business environment is shifting towards larger agricultural enterprises. This expansion of cultivated areas requires technical and technological support to provide awareness of field conditions without the constant presence of personnel. The TRACTOR project aims to develop an innovative technological solution to support precision agriculture in protected environments, utilizing autonomous and collaborative rovers, as well as generating maps that reflect monitoring data and report "anomalies" in the plants' health status.
The applications (UC) include fruit counting and monitoring of the maturation stage, along with monitoring the plant vigor index and providing support for operators during fruit harvesting. The main benefits for end-users involve targeted implementations, supported by data-driven decision-making systems, which promise an overall improvement in product quality. Additionally, improvements in agricultural activity planning and control are anticipated, thus contributing to the sector's competitiveness and sustainability, with potential advantages in terms of efficiency and timeliness in fruit harvesting.